A naïve way of looking at fuzzy sets

Trillas, Enric; Termini, Settimo; Moraga, Claudio

doi:10.1016/j.fss.2014.07.016

Cited by 12 publications

(1 citation statement)

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“…This makes FST a valid candidate to represent perceptive concepts, i.e. concepts that are formed by an act of perception and are usually designated by terms drawn from natural language [13,14,15]. Fuzzy sets can be used to represent information granules, i.e.…”

Section: Fuzzy Sets As Information Granulesmentioning

confidence: 99%

A Granular Computing Method for OWL Ontologies*

Lisi

Mencar

2018

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We propose a method to extract and integrate fuzzy information granules from a populated OWL ontology. The purpose of this approach is to represent imprecise knowledge within an OWL ontology, as motivated by the fact that the Semantic Web is full of imprecise and uncertain information coming from perceptual data, incomplete data, data with errors, etc. In particular, we focus on Fuzzy Set Theory as a means for representing and processing information granules corresponding to imprecise concepts usually expressed by linguistic terms. The method applies to numerical data properties. The values of a property are first clustered to form a collection of fuzzy sets. Then, for each fuzzy set, the relative σ-count is computed and compared with a number of predefined fuzzy quantifiers, which are therefore used to define new assertions that are added to the original ontology. In this way, the extended ontology provides both a punctual view and a granular view of individuals w.r.t. the selected property. We use a real-world ontology concerning hotels and populated with data of the Italian city of Pisa, to illustrate the method and to test its implementation. We show that it is possible to extract granular properties that can be described in natural language and smoothly integrated in the original ontology by means of annotated assertions.

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Section: Fuzzy Sets As Information Granulesmentioning

confidence: 99%